On Wed, Nov 25, 2009 at 14:18, Nick Wedd <n...@maproom.co.uk> wrote: >>> If playing one move lead 10% of time to +10, and 90% to -20, >>> the resulting value is -17 >>> (of course with the bot evaluation/playout) >> >> Reducing the value to -17 is losing a lot of information. Another move >> might have 20% chances of +10 and 80% chances of -24 giving -17, are >> they really just as good? > > If you are using Hahn scoring, yes, they are just as good. With any other > form of scoring, the lost information matters.
Ok, then we are probably having completely different mental models of what we are talking about :-) What I am considering is a way to analyze a list of moves, each having in turn a value that is a list of expected outcomes and their respective estimated probabilities, and to sort the moves by the expected outcome in the context of a given risk strategy. In practice, this means that the strategy is an algorithm that takes an outcome/probability list and converts it to a number, so that it can be compared to the other values. The algorithm in the example above is a linear weighted sum. Normal MC programs look only at the number of positive and negative outcomes. These are only two possibilities. If using a more generic approach, the strategy can be parametrized and optimized (both offline and online), hopefully resulting in a better gameplay. best regards, Vlad _______________________________________________ computer-go mailing list computer-go@computer-go.org http://www.computer-go.org/mailman/listinfo/computer-go/
